RGB Lab @ MIT
@RGBLabMIT
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Official Twitter for the Gómez-Bombarelli group @MIT_DMSE | We use atomistic simulations and ML for accelerated materials design | Managed by group members
Cambridge, MA
Joined October 2022
Scientific foundation models are converging to a universal representation of matter. Come chat with us at #NeurIPS! We (@SoojungYang2 @RGBLabMIT) have an oral spotlight at the #NeurIPS #UniReps workshop and will also poster at #AI4Mat. 🖇️: https://t.co/H8ZRlpvJUj 🧵(1/5)
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We also find that previously-parameterized classical potentials model two separate anion polarization states that drastically influence resulting lithium solvation and transference.
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We introduce a low-data ML framework to predict classical interatomic potentials, strongly agreeing with experiments and DFT polymer backbone energy barriers, using orders of magnitude less DFT training—thanks to chemistry-informed symmetry and charge regularization.
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Our work on "End-To-End Learning of Classical Interatomic Potentials for Benchmarking Anion Polarization Effects in Lithium Polymer Electrolytes" is out now in Chemistry of Materials! https://t.co/JSDwQwv8VZ
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We're out. So long, and thanks for all the fish Group's Bluesky https://t.co/Olfd0dElaM Rafa's LinkedIn
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Slightly less surprising than you'd think. We ran into the same wall three years ago
iopscience.iop.org
Molecular machine learning with conformer ensembles, Axelrod, Simon, Gómez-Bombarelli, Rafael
The surprising ineffectiveness of molecular dynamics coordinates for predicting bioactivity with machine learning 1. The study challenges the assumption that molecular dynamics (MD)-derived coordinates are superior for machine learning-based bioactivity predictions, revealing
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📢New preprint out! We constrain the molecular generation space to follow the "symmetry" of patented molecules that are likely to be synthesizable. Achieved with "symmetry-aware" fragment decomposition, and a constrained Monte Carlo Tree Search generator. https://t.co/NWidW2Wx9y
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Zero-shot extrapolation for out-of-distribution (OOD) chemical property prediction is an important step towards high-performance materials discovery. Check out our spotlight at the #NeurIPS AI for Accelerated Materials Design Workshop! https://t.co/wHxezk4zD7
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I will always upvote bogus enthalpy-entropy compensation. My thesis advisor, J Casado, loved this paper. I remember doing the calculations in undergrad kinetics class some 20 years ago
Instead of the typical (somewhat technical) lecture, we discussed a couple of papers, including a favorite J. Chem. Ed. activity of mine: "Chemistry from Telephone Numbers: The False Isokinetic Relationship." It's a lot of fun. Do check it out. 2/3 https://t.co/uvFEdlESXK
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MERGED NETS 💎📄 I can’t describe 350+ new nets in 280 signs, so just watch the animation & read our new paper in @ScienceMagazine 👨🎨💎👨🔬 with @Eddaoudi_FMD3. In short, we merge nets together and get new topologies perfect for designing mix-ligand #MOFs. https://t.co/xnARltj9dO
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Applying to DMSE? The DMSE Application Assistance Program (DAAP) offers support for students from underrepresented groups in science and engineering. You’ll be paired with a grad student mentor to guide you through the application process. Apply by Nov 1.
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In tomorrow’s Wulff Lecture, DMSE’s Professor Antoine Allanore will explore greener iron and steel production processes, highlighting innovations that use electricity instead of carbon. October 23 | 4 pm | 6-120 https://t.co/Ngj6F32juC
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Code💻: https://t.co/o2VV1pjJMV HuggingFace paper page 📰: https://t.co/55fsBHOAgu Shout out again to my amazing collaborators 🙌🙌🙌 and @thjashin for insightful discussion ❤️❤️❤️! (11/11)
huggingface.co
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Discrete generative models use denoisers for generation, but they can slip up. What if generation *isn’t only* about denoising?🤔 Introducing DDPD: Discrete Diffusion with Planned Denoising🤗🧵(1/11) w/ @junonam_ @AndrewC_ML @HannesStaerk @xuyilun2 Tommi Jaakkola @RGBLabMIT
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Final group photo of the @cecamEvents workshop on "Advances in catalytic reactivity simulations under operando conditions"! Thanks to all participants for your valuable contributions and stimulating discussions and to @IITalk @fondazione_fair for support & CASALE SA for sponsor
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Please help repost and spread the word. 🙏 My research group at @UBuffalo's Materials Design & Innovation (@UBengineering & @UBCAS) is recruiting multiple graduate students! https://t.co/jtFN49lQOy If you are interested in combining 🤖 data science and machine learning with ⚛️
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📢 New preprint alert! Our latest work @RGBLabMIT, “Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks,” is out on arXiv: https://t.co/3GAe6oH0Rh ⚛️ Chemical ordering in crystalline materials, ranging from well-ordered elemental arrangements to
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🚨 New release! Our VSSR-MC code for sampling material surface reconstructions at the atomic scale is now more modular, readable, and user-friendly. 🔧✨ Check out the code on GitHub: https://t.co/4x3kbEHsaf Paper: https://t.co/WRyDoOSbeI Feedback and contributions welcome!
github.com
MCMC-based algorithm for sampling surface reconstructions - learningmatter-mit/surface-sampling
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I am happy to share that our paper "Learning a reactive potential for silica-water through uncertainty attribution" was published in @NatureComms: https://t.co/GyiU9cvDWn
@RGBLabMIT
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